This document discusses the frontiers of computing and predictive oncology. It provides background on the speaker and changes in computing and oncology driven by improved technology and data availability. Cancer is now defined more by underlying molecular characteristics than anatomy. Team science and open data are critical to build predictive models from large datasets and present analysis and results in a timely, human-friendly way to support treatment decisions. Key challenges include improving interoperability, validating algorithms, scaling infrastructure for large data, and reducing cognitive load in data presentation to aid decision making.

1. Frontiers of Computing and
Predictive Oncology
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe

2. Outline
• Computing and Oncology
• Access to Data
• Some questions

3. Personal & Professional Background
• PhD in Chemistry at Caltech, Postdoc in
molecular genetics of RAS
• Cancer research for 20+ years - cancer
informatics, data science, healthcare
• Faculty in the Feinberg School of Medicine
at Northwestern for 15+ years
• Director NCI CBIIT 2013-2017; Acting NCI
Deputy Director 2016-2017
• Lost three grandparents to cancer

4. Changes in Computing
• Converged devices
• Converged IT
• Ubiquity of devices, data, mHealth

5. Changes in Oncology
• Cancer is a grand challenge
• Anatomic vs molecular classification
• Health vs Disease

6. (10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
Cancer is a grand challenge
• Deep biological understanding
• Advances in scientific methods
• Advances in instrumentation
• Advances in technology
• Data and computation
• Mathematical models
Cancer Research and Care generate
detailed data that is critical to
create a learning health system for cancer
Requires:

7. This redefinition has been driven by improved biological understanding

8. This change has been driven by improved technology - sequencing, imaging,
nanotech, drug developing, computing and the availability of data about
patient response to therapy

9. Health vs Disease
• What is ’normal’?
• Systematic and measurement error
• Biological heterogeneity

10. Team Science is critical
Clinical Trials
Biostatists
Bioinformatics
Clinical Care
Clinical Research
EHRs, Imaging, Lab Systems
Data Science
Analytics and Visualization
Open Data enhances collaboration and team science!

11. What we need
• Ability to build, analyze, validate
predictive models
• Well annotated and appropriate data
sets to use for building, analyzing,
and validating predictive models
• Ability to present information in a
human relevant, human informative
way
Late analysis results do not result in changes in treatment

12. Scale is changing!

13. 4/3/2017

14. Expert Systems vs Machine Learning
• In 1945, the British philosopher Gilbert
Ryle identified two kinds of knowledge—
factual, propositional knowledge that can
be ordered into rules—“knowing that.”
versus implicit, experiential, skill-based—
“knowing how.”
• Machine Learning is based on ‘learning
how’. Expert systems, or rule based
machines, are based on ‘knowing that’.

15. Human Cognition
Three kinds of learning:
• Learning that – rule-based knowledge
• Learning how – experiential knowledge
• Learning why – integrative, explanatory
knowledge

16. Questions
• How do we make data machine
readable?

17. Questions
• How do we improve interoperability
between instruments, data types,
locations, organizations?

18. Questions
• How do we build, improve, validate
algorithms, tools, pipelines?

19. Questions
• How do we scale our infrastructure,
processing, analytics to provide
timely data?

20. Questions
• How do provide data in a cognitively
friendly way that helps humans make
decisions?
• We need to:
– Reduce cognitive load
– Improve data access & ‘show your work’
– Enable timely decision support

23. Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe